Cancer remains one of the leading causes of mortality worldwide, driving the need for innovative approaches in research and treatment. Artificial intelligence (AI) has emerged as a powerful tool in oncology, with the potential to revolutionize cancer diagnosis, treatment, and management. This paper reviews recent advancements in AI applications within cancer research, focusing on early detection through computer-aided diagnosis, personalized treatment strategies, and drug discovery. We survey AI-enhanced diagnostic applications and explore AI techniques such as deep learning, as well as the integration of AI with nanomedicine and immunotherapy for cancer care. Comparative analyses of AI-based models versus traditional diagnostic methods are presented, highlighting AI’s superior potential. Additionally, we discuss the importance of integrating social determinants of health to optimize cancer care. Despite these advancements, challenges such as data quality, algorithmic biases, and clinical validation remain, limiting widespread adoption. The review concludes with a discussion of the future directions of AI in oncology, emphasizing its potential to reshape cancer care by enhancing diagnosis, personalizing treatments and targeted therapies, and ultimately improving patient outcomes.

Greater than 650 million individuals worldwide are categorized as obese, which is associated with significant health, economic, and social challenges. Given its overlap with leading comorbidities such as heart disease, innovative solutions are necessary to improve risk prediction and management strategies. In recent years, artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools in healthcare, offering novel approaches to chronic disease prevention. This narrative review explores the role of AI/ML in obesity risk prediction and management, with a special focus on childhood obesity. We begin by examining the multifactorial nature of obesity, including genetic, behavioral, and environmental factors, and the limitations of traditional approaches to predict and treat morbidity associated obesity. Next, we analyze AI/ML techniques commonly used to predict obesity risk, particularly in minimizing childhood obesity risk. We shift to the application of AI/ML in obesity management, comparing perspectives from healthcare providers versus patients. From the provider’s perspective, AI/ML tools offer real-time data from electronic medical records, wearables, and health apps to stratify patient risk, customize treatment plans, and enhance clinical decision making. From the patient’s perspective, AI/ML-driven interventions offer personalized coaching and improve long-term engagement in health management. Finally, we address key limitations and challenges, such as the role of social determinants of health, in embracing the role of AI/ML in obesity management, while offering our recommendations based on our literature review.

This literature review explores the association between hypertension and major neurocognitive disorders, including delirium, Alzheimer’s disease, vascular dementia, Lewy body dementia, and frontotemporal dementia, which contribute significantly to global mortality and morbidity. Hypertension is a potentially modifiable risk factor for cognitive decline, as it contributes to the progression of neurodegenerative pathologies via vascular damage, inflammation, and the disruption of the blood–brain barrier. Despite this, the effectiveness of antihypertensive treatments in preventing or alleviating cognitive decline remains contentious. While some research highlights the potential benefits of angiotensin-converting enzyme inhibitors and angiotensin receptor blockers, other studies show inconsistent results, complicated by variations in hypertension definitions, diagnostic criteria for cognitive disorders, and confounding factors like medication adherence. Furthermore, the complex bidirectional relationship between hypertension and major neurocognitive disorders warrants more investigation, as cognitive decline can exacerbate cardiovascular risks through heightened inflammatory responses and compromised autonomic regulation. This review underscores the need for prospective, long-term studies to elucidate the relationships between hypertension and cognitive disorders and to evaluate the potential therapeutic benefits of antihypertensive treatments.